Deliverables Monster prototype This is the main result of the project, the complete prototype with all its characteristics (SW modules, HW structures and various SW management) that will be owned by the SMEs Consortium. This is a low-cost system that will provide its users (airports, neighbouring communities, planners) a set of noise-related services, so to let them to better deal with the airport noise problem. The Monster system is based on two main components: a software module and a hardware network of sensors; the combination of these two components will enable users to design and manage the network of noise sensors minimising their number. The prototype has been already field-tested at end-users sites (Napoli: Capodichino Airport and Trieste: Ronchi dei Legionari Airport) according to the evaluation criteria and by taking into account the European standards. This assessment reached the best lay-out of the Net of Sensors, optimised in the number and in the cost (class) maintaining the established accuracy of the system. Monster Optimisation Environment (MOE) The MOE (Monster Optimisation Environment) is a SW tool part of the MONSTER noise monitoring system and is available as a stand alone SW programme. The MOE is a module of the MONSTER system, dedicated to the development of an innovative approach for the exploitation of noise information measured by remote monitoring stations scattered over a given airport control area, finalised to the concurrent maximisation of the use of such information and the optimisation of the deployment layout of remote monitoring stations targeting primarily the minimisation of their number. In the framework of the MONSTER research project, the MOE has been devised as one of the most innovative items, and its development has been based on the implementation of a novel approach for the exploitation of the information made available by a number of remote noise monitoring stations. Given an airport area to be monitored, it is usually of interest the construction of time averaged noise indexes, like the SEL (sound exposure level) for specific points or for the whole area, or the measure of peak noise levels in specific points, which can be used to blame airplanes not respecting flight traffic gates allowed by airport authorities for noise abatement reasons. The finalities of the MOE consist in optimising sensors (microphones) deployment over the territory aimed at airplane noise monitoring, according to the following objectives: - Reduce the number of microphones. - Use where acceptable microphones of reduced performance (measurement accuracy). The type of optimisation implemented, is clearly related to the approach used to exploit noise measures and to generate time averaged noise indexes. For this reason microphones deployment optimisation and the mathematical engine used to correlate microphones readings with airplane position are both developed in the MOE. The MOE incorporates one further and very innovative functionality, i.e. the capability to use the information about noise recorded by microphones in order to derive information about the position occupied by the noise source (the airplane) and generate related route; this function is very important because the approach in reducing noise levels around airports consists in limiting the access to and from runway in specific gates implementing noise abatement routes. This capability of the MOE system therefore allows to blame not complying planes on the basis of the information by microphones only. The airplane generates a volume where its noise effects are present, in motion with it, and correlated to its position. Once we are able to build this volume starting from a reduced dataset from microphones, we have information about airplane position. The MOE proposes to experiment a very innovative approach to deal with the problem of airport noise monitoring; its mathematical core has been designed in order to implement a twofold function: - Off line design tool to design a microphones deployment layout including the lowest number of stations, exploiting the MOE mathematical module to perform tasks. - On line airplane localisation and generation of noise maps extrapolation of the dataset achieved by a reduced number of microphones, to generate noise information over the whole control area At a given instant, microphones measure noise levels generated by the flight of a given airplane. Noise propagation algorithms are included in the MOE and are reverted to identify noise sources compatible with the noise measured; algorithms integrate meteorological and wind information recorded by the Remote Terminal Units; algorithms embed approximated effects due to the fact that the noise source is in motion and reflection effects from the ground. This set of measures, performed by a limited number of microphones at a given instant, does not necessarily correspond univocally to one position of the sound source (the airplane); multiple mathematical solutions are possible; in order to restrict the number of solutions the MOE implements the following: - Boundary conditions are placed in the design stage of the airport monitoring system in order to restrict the range of solutions; for instance, at a given distance from the runway, an approaching airplane can be found in a limited range of heights from the ground. - Noise measures are repeated in successive time instants, further reducing the number of possible solutions until convergence is achieved Once the noise source is localised, the same noise propagation algorithms are used to estimate noise levels in every requested point of the control surface; time averaged noise indexes, like the SEL, are then easily generated. Information is then exchanged with the MONSTER main SW application, providing estimated airplane route and noise indexes. The system can calculate if the airplane flight track complies or not with noise abatement routes. Airplane footprint In civil aviation, the impact of aircraft noise is a prime issue receiving more regulatory and technological attention than any other aviation environmental problem. It is currently one of main bottlenecks for further growth, that constraints aircraft noise emissions during specific manoeuvres around airports. The result consists in the implementation of a passive (only listening) system, designed, unlike electromagnetic radar or ultrasonic sonar, to allow the identification of airplane types and manoeuvres by processing only the sound emissions. The system is composed by an algorithm for the acoustic signature identification and a dedicated neural network classifier, trained with a set of experimental aircraft noise data collected @ Naples airport of Capodichino. The applied method for aircraft acoustic signature identification employs a wavelet multiresolution analysis of noise signals and a statistical analysis of the noise events of each aircraft class. This investigation plays a crucial role to learn the system classifier, a dedicated neural network, with features parameters of reasonable size and condense, at the same time, all the peculiar characteristics of each aircraft noise. The developed system processes noise time histories of airplanes, giving as output the identified airplane and manoeuvre, together with an index of the percentage of successful identification. It s an on-line system for noise events processing and requires a collection of aircraft noise events to learn the system classifier. The algorithm input is the noise time history of the airplane to be identified; identification task is possible only for airplane types and manoeuvres for which the classifier has been trained. The algorithm has been developed under MATLAB programming environment with a friendly Graphical User Interface. It is a stand alone executable application that requires only Matlab runtime installation on the target machine; it needs MATLAB license with Neural Network toolbox only for new classifier implementation (i.e. trained with a new or extended set of experimental noise data). The algorithm test and validation have been performed for different airplanes during take-off and landing manoeuvres. For this purpose both numerical simulation and experimental measurement of aircraft noise emissions have been analysed. A preliminary evaluation of the developed algorithm for acoustic signature recognition has been numerically performed by simulating different airplane noise sources. Finally an experimental activity of ground noise measurements has been carried out at Naples airport of Capodichino. More than 200 aircraft noise events of five aircraft types (Airbus A320, Boeing B737, Mc Donnel Douglas MD80, Fokker F100, Aerospatiale/Alenia ATR72), during both take off and landing manoeuvre, have been measured. It can be applied as support for the radar monitoring of the airports and for the verification of compliance with limitations of annoyance in the area around them. It can allow the surveillance of isolated or dangerous areas and the verification of compliance with peace agreements ("no-fly zone"). Exploitable results Low-cost system to monitor aircraft noise A significant number of European Airports Authorities are subject to social pressure by citizens and Public Administration to take action to reduce airport noise, but no technical solution has been so far successful. EU is going to enforce noise standards. The European Parliament has decided unanimously the 'resolution on night flights and noise pollution near airports'. The Parliament 'is concerned about the persistent and increasing noise levels at some airports which can have a serious effect on the health of local residents' and says 'the residential public should not be deprived of sleep by the pressure on commercial operations at airports in the vicinity'. The parliament calls on the Commission to draw up proposals for a Community framework on noise classification and considers several measures for noise abatement. The project's aim is to offer a short-term technical solution to monitor noise caused by air traffic in areas surrounding airports. Main objective of the proposed project Monster is the realisation of a low-cost system and a set of services directed to enable final users (airports, neighboring communities) to monitor the airport noise problem. Monster system will be based on two main components a software module and a hardware net of sensors. The Monster system starts from the application coming into a market product. A hardware/software solution, specifically designed for the acquisition and processing of noise parameters, will be designed and implemented. The noise parameters will be evaluated partly in the field, partly in the central elaboration unit, thus reducing the necessary transmission bandwidth and giving the possibility to tailor the calculated parameters to the needs of the customer The Monster system has been assembled based on off-the-shelf-components and has shown the following performance: - general good reliability of the system, - accurate noise acquisition, - good usability as a result of an intensive work of data analysis and reporting, - ability to identify the acoustic signature of airplanes. Further envisaged action include: - improvement of the system compactness and reduction of weight, - improvement of network integration, - extension of the validation of the MOE in further cases and with a large number of radar tracks, - extension of testing over a longer time-span, - agreement with a large number of airports to test and validate the Monster system in different conditions, - perform contacts with other potential clients and establish commercial agreement for the exploitation of the system over Europe. Searching for OpenAIRE data... There was an error trying to search data from OpenAIRE No results available
